Automatic clustering algorithm for fuzzy data

Wen-Liang Hung and Jenn-Hwai Yang

Journal of Applied Statistics, 2015, vol. 42, issue 7, 1503-1518

Abstract: Coppi et al. [7] applied Yang and Wu's [20] idea to propose a possibilistic k -means (P k M) clustering algorithm for LR -type fuzzy numbers. The memberships in the objective function of P k M no longer need to satisfy the constraint in fuzzy k -means that of a data point across classes sum to one. However, the clustering performance of P k M depends on the initializations and weighting exponent. In this paper, we propose a robust clustering method based on a self-updating procedure. The proposed algorithm not only solves the initialization problems but also obtains a good clustering result. Several numerical examples also demonstrate the effectiveness and accuracy of the proposed clustering method, especially the robustness to initial values and noise. Finally, three real fuzzy data sets are used to illustrate the superiority of this proposed algorithm.

Date: 2015
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DOI: 10.1080/02664763.2014.1001326

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